Abstract: Of an estimated 2.5 million people with epilepsy in the United States, close to 250,000 (~10%) have Juvenile Myoclonic Epilepsy (JME). The majority of patients with JME experience seizure onset during a neuro- developmentally vulnerable period and are at risk for long term cognitive and psychiatric comorbidities which carry significant associated socioeconomic and health-care utilization costs. Medications can yield lasting seizure control in some JME patients and mitigate the progressive neurodevelopmental consequences of chronically uncontrolled seizures, but for many JME patients medications do not adequately control seizures. Accurate early prediction of which patients will respond favorably to medications is crucial for optimizing selection of treatment options, but current methods for predicting the clinical course and response to treatment of JME remain inadequate. There exist no reliable biomarkers that predict the likelihood of drug resistance, disease progression, or the presence, nature and severity of cognitive or psychiatric consequences of JME, all of which vary widely between patients. Powerful imaging tools are now available for quantitatively characterizing structural and functional connections between brain regions that make up epileptic networks, providing a promising new approach for understanding, predicting, and treating refractory epilepsy. The Juvenile Myoclonic Epilepsy Connectome Project (JMECP) will collect detailed structural and connectivity measurements in 160 children and adolescents of age range 12-20 yrs (80 JME, 80 healthy controls) including DTI to evaluate structural connections and fMRI to evaluate dynamic network interactions and structural MRI to evaluate patterns of cortical and subcortical volume loss. The methods will closely mirror those currently used by the Human Connectome Project (HCP) to study network connectivity in healthy participants. These comparisons, based on large cohorts studied with sensitive, state-of-the-art methods, will investigate the full extent of abnormal network structure and function in JME. The data will be used to test several important hypotheses: 1) that recurring seizures lead to progressive connectivity abnormalities in JME, 2) that these connectivity abnormalities are linked to the cognitive and psychosocial dysfunction, 3) that severity of connectivity abnormalities predicts the risk of prospective decline in cognitive, psychosocial function, and in developing medically refractory seizures, 4) that connectivity abnormalities unique to participants with JME are associated with disease-related variables such as epilepsy duration, seizure type providing important novel biomarkers. Evidence supporting these hypotheses will lead directly to novel clinical tools for diagnosis & personalized management of JME patients based on quantitative imaging of connectome.